This application relates to an image acquisition and processing technique where the image is captured utilizing a sensor which has different sensitivity levels assigned to different pixels.
Image reconstruction is inherent in any number of technical areas. As an example, surveillance aircraft capture images at multiple wavelengths which must be reconstructed to provide information. These images must be captured relatively quickly, and the accuracy, spatial resolution, and dynamic range must be as high as possible.
However, a natural scene usually has a very high dynamic range, i.e., very bright and very dark areas, requiring for example 20 bits, and standard imaging sensors can acquire less, for example only 8-12 bits. A traditional imaging sensor faces the problem of missing scene details or blooming or serious distortion due to limited dynamic range.
Prior art methods to capture high dynamic range (“HDR”) images may use multiple sequential exposures to obtain multiple images at different exposures. Also, they may use multiple discrete sensor arrays with different sensitivities. Further, they may fabricate a single chip with multiple different size pixels to simultaneously capture multiple images with different exposures. In yet another approach, they have attempted to integrate light flux until a pixel reaches saturation where the integration time represents the actual irradiance. One further technique is to use logarithmic response pixels or circuits to nonlinearly extend the dynamic range of a scene. Finally, physical masks or filters have been utilized with pixel-wise attenuation levels arrayed in regular patterns.
With these techniques, the final image is obtained for display by compressing the high dynamic range constructed from the sample data.
All of the prior art approaches have disadvantages. The approach of taking multiple sequential exposures has artifacts due to motion in the scene, since each image is from a slightly different time or length of time. Further, complicated image registration is needed to generate the final image. There are also often artifacts in reconstructed images due to occlusion or mis-registration.
The multiple discrete sensor arrays have the problem that the light is split and reflected to different sensor arrays. The amount of light reaching a sensor is therefore less and this is not well-suited for low light imaging. The cost, size, weight, and power required are also increased due to the extra hardware.
Using multiple size pixels decreases resolution compared to a single size pixel and, thus, also is not ideal.
In the pixel integration time scheme, one main disadvantage is a partially opened transfer transistor could introduce an additional dark current source resulting in higher dark current shot noise. Also, drive circuits are more complex due to multiple signal reads. Additional electronics are required at correspondingly increased cost.
The logarithmic response pixels or circuit scheme has a nonlinear response that is not preferred in most applications since it makes proper color correction difficult across the full range of the sensor output.
The mask scheme has generally been provided with a regular grid mask, which results in low resolution or low quality due to imaging interpolation.
Thus, an improved high dynamic range imaging process is desirable.